FF_DS_AZ_CTS_VEC Dynamic Savings Vectorized Continuous Distribution

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This is the example vignette for function: ff_ds_az_cts_vec from the MEconTools Package. F(a,z) discrete probability mass function given policy function solution with continuous savings choices, vectorized.

Test FF_DS_AZ_CTS_VEC Defaults

Call the function with defaults. By default, shows the asset policy function summary. Model parameters can be changed by the mp_params.
%mp_params
mp_params = containers.Map('KeyType','char', 'ValueType','any');
mp_params('fl_crra') = 1.5;
mp_params('fl_beta') = 0.94;
% call function
ff_ds_az_cts_vec(mp_params);
Elapsed time is 2.185467 seconds. ---------------------------------------- xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx CONTAINER NAME: mp_ffcmd ND Array (Matrix etc) xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx i idx ndim numel rowN colN sum mean std coefvari min max _ ___ ____ _____ ____ ____ _____ ______ ______ ________ ___ ______ ap 1 1 2 3000 200 15 42703 14.234 14.307 1.0051 0 51.591 xxx TABLE:ap xxxxxxxxxxxxxxxxxx c1 c2 c3 c4 c5 c11 c12 c13 c14 c15 ______ ______ ______ ______ ______ _______ _______ ______ ______ ______ r1 0 0 0 0 0 0.58655 0.89911 1.2884 1.7803 2.3861 r2 0 0 0 0 0 0.58671 0.89914 1.2885 1.7804 2.3862 r3 0 0 0 0 0 0.5871 0.89961 1.2888 1.7808 2.3867 r4 0 0 0 0 0 0.58803 0.90058 1.2898 1.7817 2.3877 r5 0 0 0 0 0 0.58953 0.90208 1.2914 1.7831 2.3891 r196 45.655 45.699 45.725 45.798 45.889 47.025 47.404 47.828 48.358 49.028 r197 46.257 46.303 46.326 46.401 46.492 47.626 48.005 48.432 48.965 49.651 r198 46.863 46.91 46.931 47.007 47.097 48.232 48.611 49.041 49.59 50.294 r199 47.472 47.521 47.542 47.617 47.711 48.843 49.222 49.658 50.235 50.94 r200 48.088 48.134 48.157 48.232 48.326 49.459 49.841 50.311 50.885 51.591 FF_DS_AZ_CTS_LOOP finished. Distribution took = 0.13145 ---------------------------------------- xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx CONTAINER NAME: mp_ddcmd ND Array (Matrix etc) xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx i idx ndim numel rowN colN sum mean std coefvari min max _ ___ ____ _____ ____ ____ ___ __________ _________ ________ __________ ________ fa 1 1 2 200 200 1 1 0.005 0.0096174 1.9235 0 0.11604 faz 2 2 2 3000 200 15 1 0.00033333 0.0011636 3.4908 0 0.032295 fz 3 3 2 15 15 1 1 0.066667 0.076895 1.1534 6.1035e-05 0.20947 xxx TABLE:fa xxxxxxxxxxxxxxxxxx c1 __________ r1 0.11604 r2 0 r3 0.0004751 r4 0.00026799 r5 0.0029727 r196 3.5618e-14 r197 2.1735e-14 r198 1.329e-14 r199 8.3938e-15 r200 8.2751e-15 xxx TABLE:faz xxxxxxxxxxxxxxxxxx c1 c2 c3 c4 c5 c11 c12 c13 c14 c15 __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ r1 4.1559e-05 0.00053618 0.0031141 0.010616 0.023097 9.8338e-05 8.1894e-06 4.3385e-07 1.3284e-08 1.7934e-10 r2 0 0 0 0 0 0 0 0 0 0 r3 2.0452e-10 1.1226e-08 2.5837e-07 3.2065e-06 2.2865e-05 1.2294e-06 1.0693e-07 5.8481e-09 1.8347e-10 2.5249e-12 r4 8.6656e-10 2.8074e-08 3.684e-07 2.7287e-06 1.4098e-05 6.831e-07 5.9408e-08 3.249e-09 1.0193e-10 1.4026e-12 r5 9.2776e-08 2.9148e-06 3.479e-05 0.00019689 0.00056423 2.3628e-06 1.9305e-07 1.0072e-08 3.0458e-10 4.0697e-12 r196 1.6685e-22 7.5909e-21 1.5483e-19 1.8762e-18 1.5117e-17 7.3723e-15 8.1882e-15 6.5347e-15 3.3448e-15 8.2909e-16 r197 4.6363e-23 2.3916e-21 5.523e-20 7.5562e-19 6.8327e-18 4.5113e-15 5.0046e-15 4.0053e-15 2.0624e-15 5.148e-16 r198 8.2487e-24 4.9336e-22 1.3328e-20 2.1488e-19 2.2991e-18 2.8157e-15 3.0885e-15 2.4579e-15 1.2688e-15 3.1814e-16 r199 6.6913e-25 5.3279e-23 1.9003e-21 4.0019e-20 5.5219e-19 1.9017e-15 2.0244e-15 1.5281e-15 7.7436e-16 1.9614e-16 r200 2.8381e-26 2.725e-24 1.1911e-22 3.1319e-21 5.5136e-20 1.4819e-15 2.2618e-15 2.1457e-15 1.1964e-15 3.1409e-16 xxx TABLE:fz xxxxxxxxxxxxxxxxxx c1 __________ r1 6.1035e-05 r2 0.00085449 r3 0.0055542 r4 0.022217 r5 0.061096 r11 0.061096 r12 0.022217 r13 0.0055542 r14 0.00085449 r15 6.1035e-05
xxx tb_outcomes: all stats xxx OriginalVariableNames ap v c y coh savefraccoh ______________________ __________ ___________ __________ __________ __________ ___________ {'mean' } 1.675 5.0913 1.4673 1.467 3.1423 0.37474 {'unweighted_sum' } 42703 26797 7295.8 6979.8 49998 1657.9 {'sd' } 2.0062 1.7215 0.36267 0.51485 2.3189 0.24932 {'coefofvar' } 1.1977 0.33813 0.24717 0.35095 0.73794 0.66532 {'gini' } 0.59404 0.19113 0.13962 0.19161 0.37632 0.39022 {'min' } 0 -1.2641 0.38052 0.38052 0.38052 0 {'max' } 51.591 16.787 5.0209 6.6099 56.61 0.91805 {'pYis0' } 0.11606 0 0 0 0 0.11606 {'pYls0' } 0 0.00066766 0 0 0 0 {'pYgr0' } 0.88394 0.99933 1 1 1 0.88394 {'pYisMINY' } 0.11606 4.1559e-05 4.1559e-05 4.1559e-05 4.1559e-05 0.11606 {'pYisMAXY' } 3.1409e-16 3.1409e-16 5.148e-16 3.1409e-16 3.1409e-16 2.8381e-26 {'p0_01' } 0 -0.34507 0.45473 0.45473 0.45473 0 {'p0_1' } 0 0.52204 0.54342 0.54342 0.54342 0 {'p1' } 0 1.3412 0.6494 0.6494 0.6494 0 {'p5' } 0 2.1813 0.85431 0.77605 0.88697 0 {'p10' } 0 2.8514 0.96477 0.92741 1.002 0 {'p20' } 0.10665 3.5986 1.1516 1.0358 1.3244 0.083657 {'p25' } 0.21483 3.8501 1.2354 1.1105 1.4524 0.14274 {'p30' } 0.32994 4.2218 1.284 1.129 1.6395 0.20194 {'p40' } 0.60561 4.5759 1.3788 1.3244 1.999 0.30454 {'p50' } 0.9866 5.0443 1.4671 1.363 2.4484 0.39896 {'p60' } 1.4331 5.4957 1.5615 1.5828 2.9924 0.48032 {'p70' } 2.0261 5.9595 1.6562 1.6429 3.671 0.556 {'p75' } 2.4055 6.2377 1.7089 1.7094 4.0981 0.59225 {'p80' } 2.8929 6.5441 1.7669 1.9106 4.6329 0.62436 {'p90' } 4.3431 7.3623 1.9254 2.123 6.2699 0.69668 {'p95' } 5.7881 8.0262 2.0625 2.4019 7.7831 0.74075 {'p99' } 8.9453 9.2776 2.3421 2.9539 11.327 0.79763 {'p99_9' } 13.367 10.599 2.6636 3.7357 15.962 0.83767 {'p99_99' } 17.333 11.639 2.9483 4.3328 20.294 0.85903 {'fl_cov_ap' } 4.0248 2.8944 0.61038 0.64355 4.6352 0.41772 {'fl_cor_ap' } 1 0.83807 0.83891 0.62307 0.99637 0.83512 {'fl_cov_v' } 2.8944 2.9636 0.62238 0.79332 3.5168 0.36874 {'fl_cor_v' } 0.83807 1 0.99685 0.89507 0.88097 0.85912 {'fl_cov_c' } 0.61038 0.62238 0.13153 0.16405 0.74192 0.079746 {'fl_cor_c' } 0.83891 0.99685 1 0.87859 0.8822 0.88192 {'fl_cov_y' } 0.64355 0.79332 0.16405 0.26507 0.80761 0.079867 {'fl_cor_y' } 0.62307 0.89507 0.87859 1 0.67647 0.6222 {'fl_cov_coh' } 4.6352 3.5168 0.74192 0.80761 5.3771 0.49746 {'fl_cor_coh' } 0.99637 0.88097 0.8822 0.67647 1 0.86045 {'fl_cov_savefraccoh'} 0.41772 0.36874 0.079746 0.079867 0.49746 0.062162 {'fl_cor_savefraccoh'} 0.83512 0.85912 0.88192 0.6222 0.86045 1 {'fracByP0_01' } 0 -4.8153e-05 0.00017799 0.00018159 8.3115e-05 0 {'fracByP0_1' } 0 0.00027167 0.0013548 0.0014279 0.00063242 0 {'fracByP1' } 0 0.0032852 0.0063125 0.0069982 0.0029338 0 {'fracByP5' } 0 0.016969 0.025021 0.024262 0.011819 0 {'fracByP10' } 0 0.044207 0.05664 0.064855 0.026579 0 {'fracByP20' } 0.0026834 0.1115 0.13073 0.11733 0.067668 0.0099043 {'fracByP25' } 0.0076113 0.14492 0.17311 0.15549 0.086 0.025483 {'fracByP30' } 0.015302 0.19105 0.21762 0.19333 0.11182 0.048984 {'fracByP40' } 0.043894 0.27218 0.30467 0.27748 0.16912 0.11643 {'fracByP50' } 0.089861 0.36738 0.40369 0.36807 0.23805 0.21205 {'fracByP60' } 0.16112 0.46928 0.50828 0.46652 0.3263 0.32962 {'fracByP70' } 0.26525 0.58046 0.61519 0.57507 0.4298 0.46793 {'fracByP75' } 0.33325 0.64122 0.67431 0.63025 0.49166 0.54754 {'fracByP80' } 0.41265 0.70474 0.73277 0.69273 0.56293 0.62653 {'fracByP90' } 0.62139 0.84051 0.85792 0.82668 0.73375 0.80195 {'fracByP95' } 0.77085 0.91406 0.9245 0.90615 0.84324 0.89716 {'fracByP99' } 0.93558 0.98098 0.98317 0.97729 0.95807 0.97822 {'fracByP99_9' } 0.99103 0.99787 0.99814 0.9972 0.99438 0.99775 {'fracByP99_99' } 0.99886 0.99977 0.99979 0.99969 0.99931 0.99977

Test FF_DS_AZ_CTS_VEC Speed Tests

Call the function with different a and z grid size, print out speed:
mp_support = containers.Map('KeyType','char', 'ValueType','any');
mp_support('bl_timer') = true;
mp_support('ls_ffcmd') = {};
mp_support('ls_ddcmd') = {};
mp_support('ls_ddgrh') = {};
mp_support('bl_show_stats_table') = false;
% A grid 50, shock grid 5:
mp_params = containers.Map('KeyType','char', 'ValueType','any');
mp_params('it_a_n') = 50;
mp_params('it_z_n') = 5;
ff_ds_az_cts_vec(mp_params, mp_support);
Elapsed time is 0.459956 seconds. FF_DS_AZ_CTS_LOOP finished. Distribution took = 0.015748
% A grid 100, shock grid 7:
mp_params = containers.Map('KeyType','char', 'ValueType','any');
mp_params('it_a_n') = 100;
mp_params('it_z_n') = 7;
ff_ds_az_cts_vec(mp_params, mp_support);
Elapsed time is 0.938024 seconds. FF_DS_AZ_CTS_LOOP finished. Distribution took = 0.046035
% A grid 200, shock grid 9:
mp_params = containers.Map('KeyType','char', 'ValueType','any');
mp_params('it_a_n') = 200;
mp_params('it_z_n') = 9;
ff_ds_az_cts_vec(mp_params, mp_support);
Elapsed time is 1.696573 seconds. FF_DS_AZ_CTS_LOOP finished. Distribution took = 0.12795

Test FF_DS_AZ_CTS_VEC A grid 100 Shock grid 7

Call the function with different a and z grid size, print out speed:
mp_support = containers.Map('KeyType','char', 'ValueType','any');
mp_support('bl_timer') = true;
mp_support('ls_ffcmd') = {};
mp_support('ls_ddcmd') = {};
mp_support('ls_ddgrh') = {'faz','fa'};
mp_support('bl_show_stats_table') = true;
mp_params = containers.Map('KeyType','char', 'ValueType','any');
mp_params('it_a_n') = 100;
mp_params('it_z_n') = 7;
ff_ds_az_cts_vec(mp_params, mp_support);
Elapsed time is 0.931254 seconds. FF_DS_AZ_CTS_LOOP finished. Distribution took = 0.069571
xxx tb_outcomes: all stats xxx OriginalVariableNames ap v c y coh savefraccoh ______________________ __________ __________ __________ __________ __________ ___________ {'mean' } 3.2216 6.9329 1.5295 1.5289 4.7511 0.52357 {'unweighted_sum' } 10019 7323.6 1530.6 1473.6 11549 457.17 {'sd' } 3.2562 2.1508 0.34914 0.5307 3.5687 0.25504 {'coefofvar' } 1.0107 0.31024 0.22827 0.34711 0.75113 0.48712 {'gini' } 0.52352 0.17526 0.12797 0.19065 0.3936 0.2723 {'min' } 0 1.7008 0.58543 0.58543 0.58543 0 {'max' } 50.789 19.213 4.21 4.9969 54.997 0.92702 {'pYis0' } 0.062608 0 0 0 0 0.062608 {'pYls0' } 0 0 0 0 0 0 {'pYgr0' } 0.93739 1 1 1 1 0.93739 {'pYisMINY' } 0.062608 0.0049772 0.0049772 0.0049772 0.0049772 0.062608 {'pYisMAXY' } 2.9501e-11 2.9501e-11 3.1223e-11 2.9501e-11 2.9501e-11 1.494e-14 {'p0_01' } 0 1.7008 0.58543 0.58543 0.58543 0 {'p0_1' } 0 1.7008 0.58543 0.58543 0.58543 0 {'p1' } 0 2.9492 0.76855 0.62688 0.76855 0 {'p5' } 0 3.4945 0.97884 0.78105 1.009 0 {'p10' } 0.092835 4.1716 1.0603 0.97609 1.223 0.078835 {'p20' } 0.47609 5.1938 1.2588 1.0456 1.7419 0.27652 {'p25' } 0.7311 5.3812 1.3008 1.094 2.0576 0.35312 {'p30' } 0.97803 5.6276 1.351 1.188 2.3618 0.42581 {'p40' } 1.5512 6.3139 1.4528 1.349 3.0158 0.51932 {'p50' } 2.233 6.8328 1.5245 1.4175 3.7588 0.59714 {'p60' } 3.0801 7.416 1.6192 1.5453 4.6604 0.66085 {'p70' } 4.105 8.0461 1.7025 1.7909 5.7649 0.70987 {'p75' } 4.6992 8.4292 1.7544 1.84 6.4292 0.73355 {'p80' } 5.4329 8.7432 1.8159 1.9097 7.3478 0.75277 {'p90' } 7.7004 9.7559 1.9663 2.3407 9.5263 0.79745 {'p95' } 9.7011 10.662 2.1066 2.5036 11.722 0.82522 {'p99' } 14.279 12.148 2.3613 3.1795 16.608 0.85983 {'p99_9' } 19.899 13.734 2.6792 3.5223 22.615 0.8829 {'p99_99' } 25.265 14.885 2.9563 3.7789 28.175 0.8962 {'fl_cov_ap' } 10.603 6.2617 1.0053 1.0453 11.608 0.65544 {'fl_cor_ap' } 1 0.89408 0.8843 0.60489 0.99896 0.78925 {'fl_cov_v' } 6.2617 4.626 0.74802 0.96794 7.0097 0.47179 {'fl_cor_v' } 0.89408 1 0.99613 0.848 0.91325 0.86007 {'fl_cov_c' } 1.0053 0.74802 0.1219 0.15425 1.1272 0.078595 {'fl_cor_c' } 0.8843 0.99613 1 0.83252 0.9047 0.88265 {'fl_cov_y' } 1.0453 0.96794 0.15425 0.28164 1.1995 0.078136 {'fl_cor_y' } 0.60489 0.848 0.83252 1 0.63337 0.57729 {'fl_cov_coh' } 11.608 7.0097 1.1272 1.1995 12.735 0.73404 {'fl_cor_coh' } 0.99896 0.91325 0.9047 0.63337 1 0.8065 {'fl_cov_savefraccoh'} 0.65544 0.47179 0.078595 0.078136 0.73404 0.065046 {'fl_cor_savefraccoh'} 0.78925 0.86007 0.88265 0.57729 0.8065 1 {'fracByP0_01' } 0 0.001221 0.0019051 0.0019058 0.00061329 0 {'fracByP0_1' } 0 0.001221 0.0019051 0.0019058 0.00061329 0 {'fracByP1' } 0 0.011511 0.013437 0.0039104 0.0042425 0 {'fracByP5' } 0 0.021279 0.026546 0.024488 0.012268 0 {'fracByP10' } 0.0006892 0.05109 0.059758 0.051739 0.020676 0.0036864 {'fracByP20' } 0.0099846 0.12278 0.1366 0.12131 0.052438 0.038521 {'fracByP25' } 0.019425 0.15429 0.17945 0.15485 0.072434 0.070039 {'fracByP30' } 0.032212 0.19399 0.22206 0.19029 0.094665 0.10974 {'fracByP40' } 0.0737 0.28144 0.31482 0.27941 0.15063 0.20042 {'fracByP50' } 0.1321 0.3768 0.41124 0.37234 0.22365 0.30981 {'fracByP60' } 0.21336 0.48025 0.51513 0.4642 0.31463 0.42631 {'fracByP70' } 0.3254 0.59015 0.62157 0.57794 0.42288 0.55601 {'fracByP75' } 0.39769 0.65462 0.67967 0.6363 0.48537 0.62983 {'fracByP80' } 0.47503 0.71232 0.73844 0.70062 0.56134 0.69967 {'fracByP90' } 0.67403 0.84445 0.86104 0.82867 0.73331 0.84375 {'fracByP95' } 0.80886 0.92029 0.92647 0.90776 0.84668 0.92112 {'fracByP99' } 0.95057 0.98162 0.98401 0.97831 0.96163 0.98352 {'fracByP99_9' } 0.99336 0.99797 0.99826 0.99778 0.99494 0.99833 {'fracByP99_99' } 0.99924 0.99979 0.99981 0.99977 0.9994 0.99984

Test FF_DS_AZ_CTS_VEC A grid 300 Shock grid 25

mp_support = containers.Map('KeyType','char', 'ValueType','any');
mp_support('bl_timer') = true;
mp_support('ls_ffcmd') = {};
mp_support('ls_ddcmd') = {};
mp_support('ls_ddgrh') = {'faz','fa'};
mp_support('bl_show_stats_table') = true;
mp_params = containers.Map('KeyType','char', 'ValueType','any');
mp_params('it_a_n') = 300;
mp_params('it_z_n') = 25;
ff_ds_az_cts_vec(mp_params, mp_support);
Elapsed time is 7.884421 seconds. FF_DS_AZ_CTS_LOOP finished. Distribution took = 0.34095
xxx tb_outcomes: all stats xxx OriginalVariableNames ap v c y coh savefraccoh ______________________ __________ __________ __________ __________ __________ ___________ {'mean' } 3.2612 6.9497 1.5318 1.5305 4.793 0.52715 {'unweighted_sum' } 1.1043e+05 79555 16733 19751 1.2716e+05 3442.8 {'sd' } 3.3352 2.1663 0.35078 0.5359 3.6495 0.25199 {'coefofvar' } 1.0227 0.31171 0.229 0.35014 0.76143 0.47803 {'gini' } 0.52534 0.17597 0.12824 0.19145 0.39608 0.26748 {'min' } 0 -2.7616 0.25871 0.25871 0.25871 0 {'max' } 54.451 20.418 4.3301 8.7798 58.78 0.92837 {'pYis0' } 0.04941 0 0 0 0 0.04941 {'pYls0' } 0 7.3281e-05 0 0 0 0 {'pYgr0' } 0.95059 0.99993 1 1 1 0.95059 {'pYisMINY' } 0.04941 3.1163e-08 3.1163e-08 3.1163e-08 3.1163e-08 0.04941 {'pYisMAXY' } 2.8477e-13 2.8477e-13 1.121e-13 2.8477e-13 2.8477e-13 3.6157e-25 {'p0_01' } 0 0.33584 0.44588 0.42374 0.44588 0 {'p0_1' } 0 1.0287 0.51088 0.51088 0.51088 0 {'p1' } 0 2.33 0.67226 0.67069 0.67505 0 {'p5' } 0.0027154 3.5353 0.94151 0.8016 1.0088 0.002787 {'p10' } 0.11496 4.1978 1.0921 0.9095 1.2356 0.093483 {'p20' } 0.51133 5.096 1.2504 1.0657 1.779 0.28788 {'p25' } 0.75298 5.4004 1.3077 1.1577 2.0685 0.36173 {'p30' } 1.004 5.7312 1.3565 1.1951 2.3792 0.42532 {'p40' } 1.5834 6.298 1.4458 1.3352 3.0372 0.52408 {'p50' } 2.2686 6.8433 1.5287 1.441 3.7996 0.59884 {'p60' } 3.0898 7.4098 1.6132 1.5764 4.6904 0.65811 {'p70' } 4.0971 8.0297 1.7037 1.7526 5.7899 0.70877 {'p75' } 4.7228 8.3787 1.7552 1.8223 6.462 0.73135 {'p80' } 5.4827 8.7742 1.8144 1.9267 7.2769 0.75357 {'p90' } 7.7718 9.8224 1.9746 2.2406 9.6945 0.79922 {'p95' } 9.9683 10.704 2.1148 2.5163 12.048 0.82675 {'p99' } 14.759 12.325 2.3956 3.157 17.176 0.86245 {'p99_9' } 21.215 14.066 2.7525 3.9803 23.946 0.88686 {'p99_99' } 27.205 15.415 3.0759 4.7968 30.277 0.90047 {'fl_cov_ap' } 11.123 6.4528 1.0361 1.0808 12.16 0.65691 {'fl_cor_ap' } 1 0.89313 0.88563 0.60472 0.999 0.78162 {'fl_cov_v' } 6.4528 4.6928 0.75717 0.98035 7.21 0.46786 {'fl_cor_v' } 0.89313 1 0.99643 0.84447 0.91198 0.85705 {'fl_cov_c' } 1.0361 0.75717 0.12304 0.15594 1.1592 0.07767 {'fl_cor_c' } 0.88563 0.99643 1 0.82954 0.90548 0.87868 {'fl_cov_y' } 1.0808 0.98035 0.15594 0.28718 1.2368 0.077234 {'fl_cor_y' } 0.60472 0.84447 0.82954 1 0.63237 0.57192 {'fl_cov_coh' } 12.16 7.21 1.1592 1.2368 13.319 0.73458 {'fl_cor_coh' } 0.999 0.91198 0.90548 0.63237 1 0.79876 {'fl_cov_savefraccoh'} 0.65691 0.46786 0.07767 0.077234 0.73458 0.063501 {'fl_cor_savefraccoh'} 0.78162 0.85705 0.87868 0.57192 0.79876 1 {'fracByP0_01' } 0 7.2341e-06 8.9677e-05 2.5415e-05 2.8657e-05 0 {'fracByP0_1' } 0 0.00014925 0.00040034 0.00047536 0.00012777 0 {'fracByP1' } 0 0.0031002 0.004056 0.0057421 0.0012982 0 {'fracByP5' } 4.4271e-07 0.020663 0.026101 0.023318 0.010275 3.7554e-06 {'fracByP10' } 0.00081444 0.049128 0.059669 0.051817 0.020124 0.0043579 {'fracByP20' } 0.010142 0.11647 0.13733 0.1174 0.051401 0.041452 {'fracByP25' } 0.0197 0.15487 0.17845 0.15395 0.07176 0.07241 {'fracByP30' } 0.033115 0.19474 0.22243 0.19298 0.095014 0.11033 {'fracByP40' } 0.07268 0.28138 0.31442 0.27544 0.15079 0.20152 {'fracByP50' } 0.13241 0.3756 0.41097 0.36527 0.22198 0.30736 {'fracByP60' } 0.21444 0.47892 0.51282 0.46572 0.31091 0.42746 {'fracByP70' } 0.323 0.58868 0.62139 0.57261 0.41949 0.55675 {'fracByP75' } 0.39061 0.6478 0.67743 0.63129 0.48319 0.62572 {'fracByP80' } 0.46952 0.70943 0.73587 0.6919 0.55532 0.69697 {'fracByP90' } 0.66831 0.84297 0.85906 0.82754 0.72955 0.84259 {'fracByP95' } 0.80219 0.91616 0.92541 0.90507 0.84194 0.91979 {'fracByP99' } 0.94613 0.98125 0.98339 0.97711 0.95822 0.98365 {'fracByP99_9' } 0.9927 0.9979 0.99812 0.99719 0.99443 0.99831 {'fracByP99_99' } 0.99909 0.99977 0.99979 0.99967 0.99932 0.99983

Test FF_DS_AZ_CTS_VEC A grid 300 Shock grid 50

mp_support = containers.Map('KeyType','char', 'ValueType','any');
mp_support('bl_timer') = true;
mp_support('ls_ffcmd') = {};
mp_support('ls_ddcmd') = {};
mp_support('ls_ddgrh') = {'faz','fa'};
mp_support('bl_show_stats_table') = true;
mp_params = containers.Map('KeyType','char', 'ValueType','any');
mp_params('it_a_n') = 300;
mp_params('it_z_n') = 50;
ff_ds_az_cts_vec(mp_params, mp_support);
Elapsed time is 14.233149 seconds. FF_DS_AZ_CTS_LOOP finished. Distribution took = 1.2257
xxx tb_outcomes: all stats xxx OriginalVariableNames ap v c y coh savefraccoh ______________________ __________ ___________ __________ __________ __________ ___________ {'mean' } 3.2794 6.957 1.5328 1.5312 4.8122 0.52801 {'unweighted_sum' } 2.3346e+05 1.6237e+05 34668 53309 2.6813e+05 5324.8 {'sd' } 3.3623 2.1722 0.35142 0.53693 3.6772 0.25195 {'coefofvar' } 1.0253 0.31224 0.22927 0.35065 0.76415 0.47717 {'gini' } 0.52595 0.17618 0.12829 0.19144 0.3969 0.26705 {'min' } 0 -7.6866 0.12843 0.12843 0.12843 0 {'max' } 61.275 22.164 4.3849 15.657 65.657 0.93325 {'pYis0' } 0.049376 0 0 0 0 0.049376 {'pYls0' } 0 0.00011917 0 0 0 0 {'pYgr0' } 0.95062 0.99988 1 1 1 0.95062 {'pYisMINY' } 0.049376 1.1048e-15 1.1048e-15 1.1048e-15 1.1048e-15 0.049376 {'pYisMAXY' } 1.584e-18 1.584e-18 5.0847e-19 1.584e-18 1.584e-18 1.584e-18 {'p0_01' } 0 -0.20427 0.40271 0.40271 0.40271 0 {'p0_1' } 0 1.2141 0.53589 0.48816 0.53589 0 {'p1' } 0 2.3693 0.71312 0.64833 0.71312 0 {'p5' } 0.001023 3.5435 0.94895 0.80724 0.96945 0.0010781 {'p10' } 0.11645 4.2417 1.0917 0.93681 1.2501 0.095192 {'p20' } 0.50875 5.08 1.2515 1.072 1.7735 0.2902 {'p25' } 0.75899 5.4247 1.3061 1.1504 2.0649 0.36356 {'p30' } 1.0156 5.7325 1.3564 1.2011 2.3741 0.42667 {'p40' } 1.6036 6.2932 1.4459 1.3198 3.0387 0.52518 {'p50' } 2.2768 6.8406 1.5297 1.4423 3.8053 0.59933 {'p60' } 3.0945 7.4051 1.6122 1.5771 4.7002 0.6586 {'p70' } 4.113 8.0338 1.7042 1.7334 5.8225 0.70999 {'p75' } 4.7604 8.3794 1.7554 1.8278 6.4985 0.73226 {'p80' } 5.5142 8.7771 1.8143 1.9295 7.3239 0.75424 {'p90' } 7.8048 9.8378 1.9756 2.2476 9.7629 0.80013 {'p95' } 10.007 10.714 2.1161 2.5336 12.107 0.82766 {'p99' } 14.9 12.348 2.407 3.1578 17.285 0.86312 {'p99_9' } 21.501 14.13 2.7694 4.0322 24.216 0.88766 {'p99_99' } 27.735 15.514 3.1037 4.8946 30.851 0.90127 {'fl_cov_ap' } 11.305 6.5234 1.0466 1.0907 12.352 0.66084 {'fl_cor_ap' } 1 0.89316 0.88579 0.60415 0.99902 0.78009 {'fl_cov_v' } 6.5234 4.7186 0.76066 0.98362 7.2841 0.46879 {'fl_cor_v' } 0.89316 1 0.99645 0.84334 0.9119 0.85658 {'fl_cov_c' } 1.0466 0.76066 0.1235 0.15645 1.1701 0.077707 {'fl_cor_c' } 0.88579 0.99645 1 0.82914 0.9055 0.87766 {'fl_cov_y' } 1.0907 0.98362 0.15645 0.2883 1.2471 0.0772 {'fl_cor_y' } 0.60415 0.84334 0.82914 1 0.63165 0.57067 {'fl_cov_coh' } 12.352 7.2841 1.1701 1.2471 13.522 0.73855 {'fl_cor_coh' } 0.99902 0.9119 0.9055 0.63165 1 0.79716 {'fl_cov_savefraccoh'} 0.66084 0.46879 0.077707 0.0772 0.73855 0.063478 {'fl_cor_savefraccoh'} 0.78009 0.85658 0.87766 0.57067 0.79716 1 {'fracByP0_01' } 0 -7.0657e-06 2.6272e-05 3.0716e-05 8.3673e-06 0 {'fracByP0_1' } 0 8.1733e-05 0.00058172 0.0003 0.00018482 0 {'fracByP1' } 0 0.0025825 0.0055755 0.0043105 0.0017358 0 {'fracByP5' } 1.3446e-07 0.020553 0.028388 0.023343 0.0084443 1.165e-06 {'fracByP10' } 0.00082822 0.048923 0.059616 0.051792 0.020041 0.0045383 {'fracByP20' } 0.010119 0.11678 0.1368 0.1176 0.051426 0.041679 {'fracByP25' } 0.019764 0.15445 0.17846 0.15402 0.071298 0.07291 {'fracByP30' } 0.033198 0.19437 0.22195 0.19279 0.094487 0.11072 {'fracByP40' } 0.072799 0.28088 0.31405 0.27516 0.15079 0.20093 {'fracByP50' } 0.13186 0.37535 0.41129 0.36559 0.22202 0.30846 {'fracByP60' } 0.21318 0.47748 0.51316 0.46495 0.30966 0.42828 {'fracByP70' } 0.32222 0.58845 0.62103 0.57307 0.41837 0.55682 {'fracByP75' } 0.39045 0.64744 0.67785 0.63075 0.48233 0.62537 {'fracByP80' } 0.46786 0.7092 0.73555 0.69205 0.55399 0.69588 {'fracByP90' } 0.66756 0.84275 0.8587 0.82726 0.72947 0.84385 {'fracByP95' } 0.80166 0.91607 0.92521 0.90478 0.84112 0.91991 {'fracByP99' } 0.94602 0.98111 0.98335 0.97699 0.95791 0.98349 {'fracByP99_9' } 0.99264 0.99789 0.9981 0.99714 0.99438 0.99831 {'fracByP99_99' } 0.99908 0.99977 0.99979 0.99966 0.9993 0.99983